Some Impressions from R Finance 2015

The R/Finance 2015 Conference wrapped up last Saturday at UIC. It has been seven years already, but R/Finance still has the magic! – mostly very high quality presentations and the opportunity to interact and talk shop with some of the most accomplished R developers, financial modelers and even a few industry legends such as Emanuel Derman and Blair Hull.

Emanuel Derman led off with a provocative but extraordinary keynote talk. Derman began way out there, somewhere well beyond the left field wall recounting the struggle of Johannes Kepler to formulate his three laws of planetary motion and closed with some practical advice on how to go about the business of financial modeling. Along the way he shared some profound, original thinking in an attempt to provide a theoretical context for evaluating and understanding the limitations of financial models. His argument hinged on making and defending the distinction between theories and models. Theories such as physical theories of Kepler, Newton and Einstein are ontological: they attempt to say something about how the world is. A theory attempts to provide "absolute knowledge of the world". A model, on the other hand, "tells you about what some aspect of the world is like". Theories can be wrong, but they are not the kinds of things you can interrogate with "why" questions.

Models work through analogies and similarities. They compare something we understand to something we don't. Spinoza's Theory of emotions is a theory because it attempts to explain human emotions axiomatically from first principles.

The Black Scholes equation, by contrast, is a model that tries to provide insight through the analogy with Brownian motion. As I understood it, the practical advice from all of this is to avoid the twin traps of attempting to axiomatize financial models as if they directly captured reality, and of believing that analyzing data, no matter how many terabytes you plow through, is a substitute for an educated intuition about how the world is.

The following table lists the remaining talks in alphabetical order by speaker.

Presentation

Package

Package Location

1

Rohit Arora: Inefficiency of Modified VaR and ES

2

Kyle Balkissoon: A Framework for Integrating Portfolio-level Backtesting with Price and Quantity Information

Bryan Lewis' talk: More thoughts on the SVD and Finance was also notable for its exposition. Listening to Bryan you can almost fool yourself into believing that you could develop a love for numerical analysis and willingly spend an inordinate amount of your time contemplating the stark elegance of matrix decompositions.

Alexander McNeil's talk: R Tools for Understanding Credit Risk Modeling was a concise and exceptionally coherent tutorial on the subject, an unusual format for a keynote talk, but something that I think will be valued by students when the slides for all of the presentations become available.

Going out on a limb a bit, I offer a few un-researched, but strong impressions of the conference. This year, to a greater extent than I remember in previous years, talks were built around particular packages; talks 5, 7 and 8 for example. Also, it seemed that authors were more comfortable hightlighting and sharing packages that are work in progress; residing not on CRAN but on GitHub, R-Forge and other platforms. This may reflect a larger trend in R culture.

This is the year that cointegration replaced correlation as the operative concept in many models. The quants are way out ahead of the statisticians and data scientists on this one. Follow the money!

Speaking of data scientists: if you are a Random Forests fan do check out Mark Seligman's Rborist package, a high-performance and extensible implementation of the Random Forests algorithm.

Network analysis also seemed to be an essential element of many presentations. Gergely Daróczi's Shiny app for his analysis of the Hungarian interbank lending network is a spectacular example of how interactive graphics can enhance an analysis.

Finally, I'll finish up with some suggested reading in preparation for studying the slides of the presentations when they become available.